Discriminant-component eigenfaces for privacy-preserving face recognition

Thee Chanyaswad, J. Morris Chang, Prateek Mittal, S. Y. Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Over the past decades, face recognition has been a problem of critical interest in the machine learning and signal processing communities. However, conventional approaches such as eigenfaces do not protect the privacy of user data, which is emerging as an important design consideration in today's society. In this work, we leverage a supervised-learning subspace projection method called Discriminant Component Analysis (DCA) for privacy-preserving face recognition. By projecting the data onto the lower-dimensional signal subspace prescribed by DCA, high performance of face recognition is achievable without compromising privacy of the data owners. We evaluate our approach on three image datasets: Yale, Olivetti and Glasses datasets - the last is derived from the former two. Our approach can serve as a key enabler for real-world deployment of privacy-preserving face recognition applications, and provides a promising direction to researchers and private sectors.

Original languageEnglish (US)
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditorsKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781509007462
DOIs
StatePublished - Nov 8 2016
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: Sep 13 2016Sep 16 2016

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2016-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
CountryItaly
CityVietri sul Mare, Salerno
Period9/13/169/16/16

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Chanyaswad, T., Chang, J. M., Mittal, P., & Kung, S. Y. (2016). Discriminant-component eigenfaces for privacy-preserving face recognition. In K. Diamantaras, A. Uncini, F. A. N. Palmieri, & J. Larsen (Eds.), 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings [7738871] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2016-November). IEEE Computer Society. https://doi.org/10.1109/MLSP.2016.7738871